relation: http://miis.maths.ox.ac.uk/miis/723/
title: Probabilistic flood forecasting
creator: Munday, Paul
creator: Pearson, Natalie
creator: Lang, Georgina
creator: Warneford, Emma
creator: Siganporia, Zubin
subject: Environment
subject: Energy and utilities
description: The Environment Agency provides a forecasting and warning service to people at risk from flooding. However, flood forecasts are inherently un- certain. Efforts to quantify the uncertainty based on quantile regression have failed to capture the full extent of the uncertainty associated with significant flooding events.  An investigation into factors that may be correlated with the uncertainty lead to the observation that there are structural biases in the model. It is possible to remove these, and thereby reduce the mean square error of the predictions, but the benefit of this is apparent in the prediction of ’normal’ conditions, rather than in flood predictions.  Additionally, a tweak to the linear fit in the quantile regression is sug- gested which is better suited to the data.
date: 2012
type: Study Group Report
type: NonPeerReviewed
format: application/pdf
language: en
identifier: http://miis.maths.ox.ac.uk/miis/723/1/ESGI85-EA_CaseStudy.pdf
identifier:   Munday, Paul and Pearson, Natalie and Lang, Georgina and Warneford, Emma and Siganporia, Zubin  (2012) Probabilistic flood forecasting.  [Study Group Report]